Distributional Reinforcement Learning
31 papers with code • 0 benchmarks • 0 datasets
Value distribution is the distribution of the random return received by a reinforcement learning agent. it been used for a specific purpose such as implementing risk-aware behaviour.
We have random return Z whose expectation is the value Q. This random return is also described by a recursive equation, but one of a distributional nature
Benchmarks
These leaderboards are used to track progress in Distributional Reinforcement Learning
Latest papers with no code
Near Minimax-Optimal Distributional Temporal Difference Algorithms and The Freedman Inequality in Hilbert Spaces
In the tabular case, \citet{rowland2018analysis} and \citet{rowland2023analysis} proved the asymptotic convergence of two instances of distributional TD, namely categorical temporal difference algorithm (CTD) and quantile temporal difference algorithm (QTD), respectively.
Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
In this paper, we introduce a general framework on Risk-Sensitive Distributional Reinforcement Learning (RS-DisRL), with static Lipschitz Risk Measures (LRM) and general function approximation.
Uncertainty-Aware Transient Stability-Constrained Preventive Redispatch: A Distributional Reinforcement Learning Approach
In this paper, a Graph neural network guided Distributional Deep Reinforcement Learning (GD2RL) method is proposed, for the first time, to solve the uncertainty-aware transient stability-constrained preventive redispatch problem.
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model
We propose a new algorithm for model-based distributional reinforcement learning (RL), and prove that it is minimax-optimal for approximating return distributions with a generative model (up to logarithmic factors), resolving an open question of Zhang et al. (2023).
Echoes of Socratic Doubt: Embracing Uncertainty in Calibrated Evidential Reinforcement Learning
We present a novel statistical approach to incorporating uncertainty awareness in model-free distributional reinforcement learning involving quantile regression-based deep Q networks.
More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning
Second-order bounds are instance-dependent bounds that scale with the variance of return, which we prove are tighter than the previously known small-loss bounds of distributional RL.
Distributional Off-policy Evaluation with Bellman Residual Minimization
We consider the problem of distributional off-policy evaluation which serves as the foundation of many distributional reinforcement learning (DRL) algorithms.
Distributional Reinforcement Learning-based Energy Arbitrage Strategies in Imbalance Settlement Mechanism
Our proposed control framework takes a risk-sensitive perspective, allowing BRPs to adjust their risk preferences: we aim to optimize a weighted sum of the arbitrage profit and a risk measure while constraining the daily number of cycles for the battery.
Noise Distribution Decomposition based Multi-Agent Distributional Reinforcement Learning
In this paper, we propose a novel decomposition-based multi-agent distributional RL method by approximating the globally shared noisy reward by a Gaussian mixture model (GMM) and decomposing it into the combination of individual distributional local rewards, with which each agent can be updated locally through distributional RL.
An introduction to reinforcement learning for neuroscience
We then provide an introduction to deep reinforcement learning with examples of how these methods have been used to model different learning phenomena in the systems neuroscience literature, such as meta-reinforcement learning (Wang et al., 2018) and distributional reinforcement learning (Dabney et al., 2020).
